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Due to the phenomenon of "posterior collapse," current latent variable generative models pose a challenging design choice that either weakens the capacity of the decoder or requires augmenting the objective so it does not only maximize the likelihood of the data.
Alexander A Alemi, Ben Poole, Ian Fischer, Joshua V Dillon, Rif A Saurous, and Kevin Murphy · 1938
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Generating Sentences by Editing Prototypes
Kelvin Guu, Tatsunori B Hashimoto, Yonatan Oren, and Percy Liang · 1938
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Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 1938
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Slow feature analysis: Unsupervised learning of invariances
Laurenz Wiskott and Terrence J. Sejnowski · 2002
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Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Thomas M. Cover and Joy A. Thomas · 2006
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A maximum-likelihood interpretation for slow feature analysis
R. E. Turner and M. Sahani · 2007
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Visualizing high-dimensional data using t-sne
L.J.P. van der Maaten and G.E. Hinton · 2008
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, and Phillipp Koehn · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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Associative Compression Networks for Representation Learning
Alex Graves and Jacob Menick · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Generating Sentences from a Continuous Space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Jozefowicz, and Samy Bengio · 2015
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A Recurrent Latent Variable Model for Sequential Data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron Courville, and Yoshua Bengio · 2015
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
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Lei Jimmy Ba, Ryan Kiros, and Geoffrey E. Hinton · 2016
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Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Towards conceptual compression
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, and Daan Wierstra · 2016
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Jakub M. Tomczak and Max Welling · 2017
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Neural discrete representation learning
Aäron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Improved Variational Autoencoders for Text Modeling using Dilated Convolutions
Zichao Yang, Zhiting Hu, Ruslan Salakhutdinov, and Taylor Berg-Kirkpatrick · 2017
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InfoVAE: Balancing Learning and Inference in Variational Autoencoders
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PixelVAE: A Latent Variable Model for Natural Images
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga, Francesco Visin, David Vazquez, and Aaron Courville · 2016
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ELBO surgery: yet another way to carve up the variational evidence lower bound
Matthew D Hoffman, Matthew J Johnson, and Google Brain · 2016
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Improving Variational Inference with Inverse Autoregressive Flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
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Stochastic variational video prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, and Sergey Levine · 2017
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PixelSNAIL: An Improved Autoregressive Generative Model
Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, and Pieter Abbeel · 2017
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β \beta -VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
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Uncertainty in the variational information bottleneck
Alexander A Alemi, Ian Fischer, and Joshua V Dillon · 2018
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Hyperspherical variational auto-encoders
Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and Jakub M Tomczak · 2018
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Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
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Taming VAEs
D. Jimenez Rezende and F. Viola · 2018
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Semi-amortized variational autoencoders
Yoon Kim, Sam Wiseman, Andrew Miller, David Sontag, and Alexander Rush · 2018
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Autoregressive quantile networks for generative modeling
Georg Ostrovski, Will Dabney, and Remi Munos · 2018
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Theory and experiments on vector quantized autoencoders
Aurko Roy, Ashish Vaswani, Arvind Neelakantan, and Niki Parmar · 2018
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Tensor2tensor for neural machine translation
Ashish Vaswani, Samy Bengio, Eugene Brevdo, François Chollet, Aidan N. Gomez, Stephan Gouws, Llion Jones, Lukasz Kaiser, Nal Kalchbrenner, Niki Parmar, Ryan Sepassi, Noam Shazeer, and Jakob Uszkoreit · 2018
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Spherical latent spaces for stable variational autoencoders
Jiacheng Xu and Greg Durrett · 2018
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